### Challenge periods
The challenge is based on images acquired by a KFM from samples collected by using several devices. Each FLOTAC/Mini-FLOTAC sample generates very high-resolution images, split in hundreds of overlapping patches, saved in jpeg or png format. This results in thousands of images made available for the competition, unpublished and not provided to other people before. Each patch may or may not contain parasites’ eggs. For training data, participants are also provided with ROI (region of interest) files containing, for each image, the coordinates of the bounding box of parasites within it, as identified and segmented by an expert operator. Test data is provided without the ROI files. The use of third-parties publicly available datasets, from the same domain or from different ones, is allowed, but their use must be declared to the challenge organizers. 

Training and test datasets belong to different acquisition samples (i.e. different FLOTAC and/or Mini-FLOTAC devices). In particular, the test will happen in two different moments: 

1. during the first period of the competition (day 1 up to 1 day before the deadline), a test sample (images coming from a single FLOTAC and/or Mini-FLOTAC device) will be made available for participants to test the performance of their models;
2. on the last day, a new test dataset (with images coming from more than one FLOTAC and/or Mini-FLOTAC device) will be released. THIS WILL BE THE DATA USED TO DETERMINE THE WINNERS. 

A subset of the test data will be used for the public leaderboard, while the remaining part will be only used for the private leaderboard. During the first period, 100% of the data will be used for the public leaderboard, while during the second period (the last day), only 5% of the data will be used for the public leaderboard. Teams will be allowed a maximum of 1 submission per day. This means that they will have a single submission on the last day to submit a prediction for the real test set. 

The submission will consist of a .csv file where the participants have to report, on different lines and for each image, a pixel-encoded binary mask (please refer to the evaluation page for details) identifying all the detected eggs (if any).